Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on Yellow Saddle Goatfish Algorithm
Round 1
Reviewer 1 Report
This paper proposed a new clustering scheme for seeking energy-efficient clustering routing protocol. The subject is very interesting, please check the below comments and add some materials if possible.
1) First of all, the results of proposed algorithm and DEEC show very similar trend (the patterns of all outputs such as alive nodes, dead nodes, energy is similar). But the reviewer can not find the reason because there is no any theoretical or mathematical descriptions of other compared algorithms.
2) In addition, there are no description regarding the calibrated process for parameters of other compared algorithms.
3) Please explain in detail, what is the main different points compare with other meta-heuristic algorithms such as PSO and ACO (e.g. particle based mata-heuristic algorithms)
Minor) The scheme is based on the Yellow Saddle Goatfish Algorithm (YSGA). -> The scheme is developed based on the Yellow Saddle Goatfish Algorithm (YSGA).
Author Response
Dear
Editor
Mathematics Editorial Office
We are submitting the paper:
“Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks based on Yellow Saddle Goatfish Algorithm”
Authored by: Carolina Del-Valle-Soto*, Alma Rodríguez, Ramiro Velázquez.
We would like to thank the reviewers and editors for their detailed analysis of the manuscript; the comments are very valuable to us. In the revised version of the paper, we have incorporated all the changes recommended by the reviewers.
Comments to all observations and suggestions, including point-by-point responses, are addressed in the following text.
Reviewer 1 comments
Comment 1: This paper proposed a new clustering scheme for seeking energy-efficient clustering routing protocol. The subject is very interesting, please check the below comments and add some materials if possible.
First of all, the results of proposed algorithm and DEEC show very similar trend (the patterns of all outputs such as alive nodes, dead nodes, energy is similar). But the reviewer can not find the reason because there is no any theoretical or mathematical descriptions of other compared algorithms.
Response: Thank you for your observation.
The proposed method and DEEC might seem similar in their resulting patterns since both schemes are based on similar performing principles. Under such conditions, both techniques could generate similar rates of energy consumption in every round. However, the proposed strategy includes additional factors that influence the selection of CHs, which leads to an optimal cluster network configuration that extends the lifetime of the network. In contrast, the DEEC method obtains suboptimal cluster topologies that produce higher consumption. Moreover, selected CHs do not always have the maximum remaining energy, which leads to inefficient energy expenditure. Furthermore, cluster formation also stops quickly in DEEC, which forces normal nodes to communicate directly to the base station, causing faster exhaustion of nodes, and consequently, reducing the network lifetime.
Comment 2: In addition, there are no description regarding the calibrated process for parameters of other compared algorithms.
Response: As suggested, we have included Table 3 with the missing parameters of methods in comparison. These values were set according to the reported simulation parameters by the authors. Table 1 provides parameter values that all approaches have in common, while table 3 considers only the settings that belong to each method.
Comment 3: Please explain in detail, what is the main different points compare with other meta-heuristic algorithms such as PSO and ACO (e.g. particle based mata-heuristic algorithms)
Response: Thank you for your comment. In the following, we present a discussion to point out the differences between metaheuristic algorithms. This discussion has also been included in the manuscript under the introduction section:
The main difference between meta-heuristic algorithms lies in its updated operators in its search strategy. PSO, ACO, and YSGA implement different updated operators. For instance, the PSO algorithm uses the velocity as a movement operator, which considers the information of the global best and local best particles to update the position of the individuals. On the other hand, the ACO algorithm is based on the quantity of pheromone laid by every ant to compute a probability used to update candidate solutions. In contrast, YSGA implements Lévy flights to update positions of chaser agents, while logarithmic spirals are used to update the location of blocker individuals. Furthermore, different from PSO and ACO, YSGA considers groups of particles to guide the search through different directions while covering more land space. Despite their bio-inspired nature, the distinct updated mechanisms that every metaheuristic algorithm implements are what made them completely different.
The difference between the proposed YSGA clustering protocol and other protocols that also implement metaheuristic algorithms is the strategy to select CHs. In our method, we have encoded candidate solutions to automatically choose the number of CHs and avoid a fixed percentage of CHs or predefined probabilities to select CHs. Our strategy ensures the optimal number of CHs is chosen in every round by the implementation of the YSGA. Furthermore, the proposed method includes a different cost function to guide the search towards the clustering network configuration that better fist the requirements of energy savings and load balancing.
Comment 4: Minor) The scheme is based on the Yellow Saddle Goatfish Algorithm (YSGA). -> The scheme is developed based on the Yellow Saddle Goatfish Algorithm (YSGA).
Response: Thank you for noticing. In the edited version of the manuscript, we have changed the sentence, as the reviewer suggested. The edited sentence can be found in the second last paragraph of the introduction section. This sentence used to be also in the abstract. However, it was removed from the abstract since the abstract was also edited.
Thank you very much.
Sincerely,
Carolina Del-Valle-Soto
Corresponding author
Universidad Panamericana. Facultad de Ingeniería. Álvaro del Portillo 49, Zapopan, Jalisco, 45010, México.
Phone: +52 (33) 13682200 | Ext. 4866
Email: [email protected]
Author Response File: 
Author Response.pdf
Reviewer 2 Report
What is the unit for "Sensing area"?
What is the plan for Future Work? It can be added within the Conclusion of the paper.
Author Response
Dear
Editor
Mathematics Editorial Office
We are submitting the paper:
“Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks based on Yellow Saddle Goatfish Algorithm”
Authored by: Carolina Del-Valle-Soto*, Alma Rodríguez, Ramiro Velázquez.
We would like to thank the reviewers and editors for their detailed analysis of the manuscript; the comments are very valuable to us. In the revised version of the paper, we have incorporated all the changes recommended by the reviewers.
Comments to all observations and suggestions, including point-by-point responses, are addressed in the following text.
Reviewer 2 comments
Comment 1: What is the unit for "Sensing area"?
Response: Thank you very much for noticing the missing information. In the revised version, we have included the unit for the sensing area in Table 1, which is m2.
Comment 2: What is the plan for Future Work? It can be added within the Conclusion of the paper.
Response: As the reviewer suggested, we have included in the conclusion section, some possible improvements that can be considered as future work. The added paragraph is presented here for convenience:
As feature work, the proposed protocol can be enhanced by including more terms in the cost function, such as considering uniform clusters sizes for a better load balancing. Additionally, the encoded candidate solutions might be arranged differently so that the dimensionality of the optimization problem can be considerably reduced. This change can increase the scalability of the sensor network. Furthermore, the off-line execution of the protocol can consider the main drawback of the proposed method. Therefore, a significant improvement can be its development in parallel to speed up the process of finding the optimal cluster configuration. This improvement will allow the protocol to run online in real-time applications.
Thank you very much.
Sincerely,
Carolina Del-Valle-Soto
Corresponding author
Universidad Panamericana. Facultad de Ingeniería. Álvaro del Portillo 49, Zapopan, Jalisco, 45010, México.
Phone: +52 (33) 13682200 | Ext. 4866
Email: [email protected]
Author Response File: Author Response.pdf
Reviewer 3 Report
In this work, a novel clustering routing protocol called YSGAP has been proposed. The method finds the optimal network configuration to reduce the energy consumption in every round and prolong the network lifetime.
The authors may need to take into consideration the following issues:
- References format is inconstantly. I suggest the author should take revise carefully. For example, the [1] and the [8] are different format.
- The abstract part is a little bit short. I suggest the author can add more paragraph such as motivation or background in this articles.
- In the line 199 (Network assumptions): I suggest the author should use the number (such as 1, 2, 3 or a , b, c ...) instead of use ●.
- In the Table 1. I suggest did not separate the one table into the two pages. Table 1 should be replaced in the other positions
- Some figures such as 1 and 2. I suggest the author should use the different line style to show the experimental results. If the reader just print this paper by B & W colors. They can not recognize all of them.
- In the figure 4. How about the 25%, 50%, dead nodes in this experiments.
Author Response
Dear
Editor
Mathematics Editorial Office
We are submitting the paper:
“Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks based on Yellow Saddle Goatfish Algorithm”
Authored by: Carolina Del-Valle-Soto*, Alma Rodríguez, Ramiro Velázquez.
We would like to thank the reviewers and editors for their detailed analysis of the manuscript; the comments are very valuable to us. In the revised version of the paper, we have incorporated all the changes recommended by the reviewers.
Comments to all observations and suggestions, including point-by-point responses, are addressed in the following text.
Reviewer 3 comments
Comment 1: In this work, a novel clustering routing protocol called YSGAP has been proposed. The method finds the optimal network configuration to reduce the energy consumption in every round and prolong the network lifetime.
The authors may need to take into consideration the following issues:
References format is inconstantly. I suggest the author should take revise carefully. For example, the [1] and the [8] are different format.
Response: The Reviewer notes a very good point. We have corrected the format of all the references to achieve a homogenization between them. The Reviewer may notice that they are already in a uniform format from [1] to [39].
Comment 2: The abstract part is a little bit short. I suggest the author can add more paragraph such as motivation or background in this articles.
Response: Thank you for your recommendation. As suggested, we have included in the abstract the motivation and background of the proposed work. The edited abstract is included here for convenience:
The usage of wireless sensor devices in a wide range of applications, such as in the Internet of Things and monitoring in dangerous geographical spaces, has increased in the last years. However, sensor nodes have limited power, and battery replacement is not viable in most cases. Thus, energy savings in Wireless Sensor Networks (WSNs) has been the primary concern in the design of efficient communication protocols. Therefore, a novel energy-efficient clustering routing protocol for WSNs based on Yellow Saddle Goatfish Algorithm (YSGA) is proposed. The protocol is intended to enhance the network lifetime by reducing energy consumption. The network considers a base station and a set of cluster heads in its cluster structure. The number of cluster heads and the selection of optimal cluster heads is determined by the YSGA algorithm, while sensor nodes are assigned to its nearest cluster head. The cluster structure of the network is reconfigured by YSGA to ensure an optimal distribution of cluster heads and reduce the transmission distance. Experiments show competitive results and demonstrate that the proposed routing protocol minimizes the energy consumption, improves the lifetime, and prolongs the stability period of the network in comparison with the stated of the art clustering routing protocols.
Comment 3: In the line 199 (Network assumptions): I suggest the author should use the number (such as 1, 2, 3 or a , b, c ...) instead of use ●.
Response: Thanks to the Reviewer for bringing the above write-up to our attention. We have changed the bullet symbols to enums.
Network assumptions:
- The network has one base station , a set of cluster heads , and a set of sensor nodes
- The power of the base station is externally supplied, while the energy of sensor nodes is limited
- A sensor node will be considered dead when it is out of power
- All sensor nodes are homogeneous
Network structure:
- Initially, all nodes are randomly deployed in the sensing area
- Nodes location will not change during the whole life of the network
- The base station is placed at the center of the sensing area
- The number of clusters is not fixed
- Every normal node (also called leaf node) is added to its nearest cluster head
Comment 4: In the Table 1. I suggest did not separate the one table into the two pages. Table 1 should be replaced in the other positions
Response: Thank you very much. We have arranged Table 1 so that it is fully visible on the same page.
Comment 5: Some figures such as 1 and 2. I suggest the author should use the different line style to show the experimental results. If the reader just print this paper by B & W colors. They can not recognize all of them.
Response: Thank you for your recommendation. As suggested, we have edited figures 1, 2, 5, 6, and 7 to ensure the reader can quickly identify the performance of the methods by a different line style.
Comment 6: In the figure 4. How about the 25%, 50%, dead nodes in this experiments.
Response: As suggested, we have included in fig. 4 the number of rounds in which the 25% and 50% of nodes are dead. Additionally, we changed color bars to grayscale so the reader can identify the performance of the methods by a different grayscale color in case the paper is impressed in black and white.
Thank you very much.
Sincerely,
Carolina Del-Valle-Soto
Corresponding author
Universidad Panamericana. Facultad de Ingeniería. Álvaro del Portillo 49, Zapopan, Jalisco, 45010, México.
Phone: +52 (33) 13682200 | Ext. 4866
Email: [email protected]
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Original reviewer's comment 1: This paper proposed a new clustering scheme for seeking energy-efficient clustering routing protocol. The subject is very interesting, please check the below comments and add some materials if possible.
First of all, the results of proposed algorithm and DEEC show very similar trend (the patterns of all outputs such as alive nodes, dead nodes, energy is similar). But the reviewer can not find the reason because there is no any theoretical or mathematical descriptions of other compared algorithms.
Author's Response: Thank you for your observation.
The proposed method and DEEC might seem similar in their resulting patterns since both schemes are based on similar performing principles. Under such conditions, both techniques could generate similar rates of energy consumption in every round. However, the proposed strategy includes additional factors that influence the selection of CHs, which leads to an optimal cluster network configuration that extends the lifetime of the network. In contrast, the DEEC method obtains suboptimal cluster topologies that produce higher consumption. Moreover, selected CHs do not always have the maximum remaining energy, which leads to inefficient energy expenditure. Furthermore, cluster formation also stops quickly in DEEC, which forces normal nodes to communicate directly to the base station, causing faster exhaustion of nodes, and consequently, reducing the network lifetime.
My new comment)
Thank you for your detail description of my review comments. Then, how these response is explained in the revised manuscript? If not, please add above description s in "Simulation and experimental results" section.
Original reviewer's comment 3: Please explain in detail, what is the main different points compare with other meta-heuristic algorithms such as PSO and ACO (e.g. particle based mata-heuristic algorithms)
Author's Response: Thank you for your comment. In the following, we present a discussion to point out the differences between metaheuristic algorithms. This discussion has also been included in the manuscript under the introduction section:
My new comment)
If the YSGA implements Lévy flights to update positions of chaser agents, may be the authors already knows, Cuckoo search(CS), developed by Xin-she Yang and Suash Deb in 2009 (https://en.wikipedia.org/wiki/Cuckoo_search), is very similar meta-heuristic algorithm becasuse they used Lévy flights concept and discovered that the random-walk style search is better performed by Lévy flights rather than simple random walk in this algorithm. Then, please add the difference between YSGA and CS.
Author Response
Dear
Editor
Mathematics Editorial Office
We are submitting the paper:
“Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks based on Yellow Saddle Goatfish Algorithm”
Authored by: Carolina Del-Valle-Soto*, Alma Rodríguez, Ramiro Velázquez.
We would like to thank the reviewers and editors for their detailed analysis of the manuscript; the comments are very valuable to us. In the revised version of the paper, we have incorporated all the changes recommended by the reviewers.
Comments to all observations and suggestions, including point-by-point responses, are addressed in the following text.
Reviewer 1 comments
New comment 1: Thank you for your detail description of my review comments. Then, how these response is explained in the revised manuscript? If not, please add above description s in "Simulation and experimental results" section.
Response: Thank you for your valuable comment.
We forgot to include the response in the previous revised manuscript. Thus, as the reviewer suggested, we have included the text in the “Simulation and experimental result” section under the 4.1 subsection.
New comment 2: If the YSGA implements Lévy flights to update positions of chaser agents, may be the authors already knows, Cuckoo search(CS), developed by Xin-she Yang and Suash Deb in 2009 (https://en.wikipedia.org/wiki/Cuckoo_search), is very similar meta-heuristic algorithm becasuse they used Lévy flights concept and discovered that the random-walk style search is better performed by Lévy flights rather than simple random walk in this algorithm. Then, please add the difference between YSGA and CS.
Response: Thank you for your appreciated comment. The reviewer can find below the detailed differences between YSGA and CS. The first paragraph of this discussion has also been included in the revised manuscript in section 2.
Although YSGA implements Lévy flights in its search strategy, like other algorithms such as Cuckoo Search (CS), there are significant differences among them. As an example, CS uses a global population of host nests for the search. On the other hand, YSGA utilizes subpopulations of search agents with blockers and a chaser in each subpopulation. Besides, the mainly updated mechanism in CS is Lévy flights, while the updated mechanisms in YSGA are Lévy flights (for exploration) and a logarithmic spiral path for exploitation. Additionally, the CS algorithm includes a selection operator, which discards the worst solutions under a certain probability to replace them with new ones. In contrast, YSGA implements other operators, such as the exchange of roles and the change of zone. These operators help to improve the solutions, so they do not need to be removed from the population by a selection mechanism. Furthermore, the exchange of roles promotes diversity in every subpopulation, while the change of zone avoids stagnation in local optima.
Summarizing, CS only uses Lévy flights to update the solutions and a probabilistic selection to remove the worst particles. In contrast, YSGA divides the population into clusters, each one guided by its search strategy. In every subpopulation, the search agents play different roles, so they are updated differently, either with Lévy flights or the logarithmic spiral path. The roles of the search agents can change in every subpopulation to improve diversity. Finally, subpopulations can move to another zone if the land space has been explored enough. This mechanism is used to avoid falling into suboptimal solutions.
Thank you very much.
Sincerely,
Carolina Del-Valle-Soto
Corresponding author
Universidad Panamericana. Facultad de Ingeniería. Álvaro del Portillo 49, Zapopan, Jalisco, 45010, México.
Phone: +52 (33) 13682200 | Ext. 4866
Email: [email protected]
Author Response File: 
Author Response.docx
